In this paper, we apply the iterative deep learning for document enhancement and the final classifier is traditional binarization method.
Unlike the traditional binarization methods which train the neural network to predict the label of each pixel, we train the neural network to learn the degradation and correct the degraded document iteratively.
The output of the neural network is the expected uniform and clean images instead of the binary maps, which allows the network to learn the degradations: the differences between the degraded and clean images.
Since the output of the neural network is the improved version of the input, it can be also fed into the network for fine-tunning.
More precisely, the learned neural network can be used recursively to refine the results because the output image can also be considered as
a lightly degraded image if the learned neural network does not provide a good result in the first iteration.
In addition, given the uniform image which is corrected by the learned neural network, the binarized image can be easily and efficiently obtained by a global threshold, such as the Otsu's threshold [1]}.
